1. Field of the Invention
The present invention relates to the detection of defects in lumber. More specifically, the invention relates to a defect detection system which uses a combination of technologies to inspect a wood board (flitch) to determine the presence and location of defects to optimize cutting of the board.
2. Related Art
Wood is the primary material from which many high-demand products are made. It is used as a structural building material, e.g., the material used to create 2.times.4s, 2.times.8s, and 2.times.12s used in framing; as a finishing building material, e.g., a material used to create trim used around doors and windows as well and a material used to fabricate doors and windows; as a packaging material, e.g., a material used to create pallets and enclosures; and as a material in making finished products, e.g., a material used to create furniture and cabinets.
The forest products industry can be broken down into a number of possible processing operations. The first operation involves cutting trees and turning them into logs. This processing operation is called logging. A second processing operation involves turning logs into a product that can be used by others. This processing operation is called primary processing or primary manufacturing. Examples of primary processing operations include sawmillers that turn logs into lumber, plywood manufacturers that turn logs into plywood, and veneer manufacturers that turn logs into veneers. A third processing operation involves turning the products created by primary manufacturers into products that are typically sold to end users, i.e., the buying public. This processing operation is called secondary processing or secondary manufacturing. Example products created by secondary manufacturers include doors, windows, cabinets, furniture, flooring, trim, and other household fixtures.
Forest products companies typically concentrate their efforts in one particular type of processing operation, i.e., a company is typically involved in either logging; or in one or more areas of primary processing; or in one or more areas of secondary processing. To accommodate this structure, rules have been established for determining the value of logs to facilitate acquisition of logs by primary manufacturers, for determining the value of lumber to facilitate the acquisition of lumber by secondary manufacturers, etc. These rules establish the grade of the material. The higher an item's grade, the higher its market value. Obviously, the end products purchased directly by consumers are not subject to grading rules, but rather individual judgments about what is aesthetically pleasing and what constitutes quality construction.
As with any other manufacturing industry, there is always a trade-off between quality and yield; the higher the quality of a product produced, the fewer the items that can be made from a given volume of raw material. The right trade-off point is, by definition, the one that optimizes the value of all the products produced from a given volume of raw material.
As perhaps in no other industry, workers' decisions markedly affect both the quality and yield of products created. Consider just the sawing operations performed in each of the above-mentioned three basic processing operations. The sawyer's goal is to remove defects from the wood material while maximizing the volume of the product produced, where a defect is defined to be any feature in wood that will affect the quality/grade of the product being produced. Sawyers typically make hundreds of sawing decisions during a given work day, each of which can and does affect the quality and volume of product produced. Where applicable, human graders make hundreds of grading decisions each work day, decisions that directly affect the market value of the items inspected. Studies have shown that employees do not always make the best decisions in saw-up processes or are not always right in their grade assignments. These errors cost manufacturers money.
Researchers from government, universities, and industry have recognized the need for automation in the forest products industry for a number of years. Hence, a good deal of work has gone into creating devices for aiding in, if not totally automating, the sawing and grading processes involved in the forest products industry. Clearly, if one is to automate any of these processes, one must develop machine vision technologies capable of locating and identifying defects. Research aimed at creating such machine vision technologies has resulted in the publication of a number of articles in the scientific literature and the issuance of a number of patents. These patents are based solely, or in part, on the sensing and processing technologies used to find defects.
To understand the nature of the present invention one must understand the scope and limitations of the work that has previously been done, for example by reviewing the available literature in an application-specific manner. The applications which are thus considered are, in order, (1) logging applications, (2) the primary processing applications, and (3) applications within the secondary manufacturing area.
There are two primary applications for machine vision technology within the logging industry. The first involves analyzing a tree stem to determine where cuts should be made to saw it into logs. Accurately making this decision involves analyzing the three-dimensional shape of the stem and inferring where log grading defects occur within the stem. A second, related application is accurately assigning the appropriate grade to the logs that are created. This problem also involves consideration of a log's three-dimensional shape and a determination of the locations of internal grading defects.
The two primary applications in the logging industry are very similar to an application in the primary processing area. This application in the primary processing area involves determining for a given log the best break-down strategy for creating either lumber or veneer. In both instances, the objective of the strategy is to maximize the value of the products that are created. In either case, the three-dimensional space must be considered, as well as the location and identity of internal grading defects. It should be noted that features affecting a log's grade are also, by and large, the features that affect the grades of lumber and veneer.
A number of researchers and inventors have developed machine vision technologies to scan external and internal features of logs. As discussed by E. M. Williston, Computer control systems for log processing and lumber manufacturing (San Francisco, Calif.: Miller Freeman Publications, 1985) ("Williston"), the most widely used log scanners in the forest products industry measure only external features and employ some type of log profile sensor. For many years, applications have been proposed to locate internal log features such as knots, decay, and pith. Examples of such applications are discussed by P. O. G. Hagman et al., "Classification of Scots pine (Pinus sylvestris) knots in density images from CT scanned logs," 53 Hols als Roh- und Werkstoff, pp. 75-81 (1995); B. V. Funt et al., "Detection of internal log defects by automatic interpretation of computer tomography images," 37 Forest Products Journal, pp. 56-62 (1987); F. W. Taylor et al., "Locating knots by industrial tomography--A feasibility study," 34 Forest Products Journal, pp. 42-46 (1984); S. J. Chang, "External and internal defect detection to optimize cutting of hardwood logs and lumber," Transferring Technologies for Industry, No. 3 (USDA, Beltsville, Md., September 1992); and P. A. Araman et al., "Machine vision systems for processing hardwood lumber and logs," 6 AI Applications, pp. 13-26 (1992) ("Araman et al."). Several recent patents also disclose scanning technologies for external and internal log features. See, for example, U.S. Pat. Nos. 4,246,940 and 4,965,734 to Edwards et al.; U.S. Pat. No. 4,831,545 to Floyd et al.; U.S. Pat. No. 5,023,805 to Aune et al.; U.S. Pat. No. 5,394,342 to Poon; and International Pat. publication No. WO 91/05245 to Sikanen et al. Commercial applications of these technologies are limited to systems that scan only the external shape of the log. This is primarily due to the very high cost of internal log scanning systems, as well their limited throughput capabilities.
There are two other related applications within the primary processing area, applications that are very different from those described above. The first of these involves log break-down. Logs can be broken down into a number of components including lumber, veneer, cants, and chips. The objective in primary processing is to decide how best to break down a log such that maximum value is attained. Typically, lumber and/or veneer are the products of choice, because they are the products with the highest market value. Maximizing volume of these products is not always analogous to maximizing value, because some grading rules for either lumber or veneer are such that they can be trimmed to a smaller size, yielding a higher-grade, higher-value product. The complexity of grading rules, along with dynamic changes in product pricing, makes it very difficult to maximize product value. Therefore, it is appropriate to be able to know the three-dimensional profile (e.g. size and shape) of the material, along with the location of grading defects on the surface of the material being processed.
Finally, an optimizing system should be able not only to locate grading defects, but also to be able to identify the type of defect present at each location. Adding the ability to classify the type of defect present further improves the quality of the processing decisions that can be made, ensuring that maximum value products can be attained.
The second related primary processing application involves accurately assigning a grade to the product produced (e.g., lumber or veneer). This application does involve some consideration of three-dimensional profile, e.g., the size, shape, and the presence of wane or holes. It also involves the inspection of product surfaces, i.e., the major faces of lumber, veneer flitches, plywood, etc. This assignment of grade generally does not involve the consideration of locations and identities of internal defects, though this is required if one is going to adequately address certain structural lumber and plywood grading problems.
Researchers have for some time understood the importance of creating methods for automating primary processing tasks. In particular, a good deal of work has gone into automating the edging and trimming operations done to create softwood structural lumber. Most of this work has concentrated on using laser ranging devices or optical profiling devices to locate board profile or wane on softwood flitches, as discussed by Williston, and has resulted in several patents, most notably U.S. Pat. No. 4,541,722 to Jenks; U.S. Pat. No. 4,188,544 to Chasson; U.S. Pat. No. 5,142,955 to Hale; U.S. Pat. No. 4,468,992 to McGeehee; U.S. Pat. No. 4,123,169 to Merilainen et al.; U.S. Pat. No. 4,207,472 to Idelsohn et al.; U.S. Pat. No. 4,221,974 to Mueller et al.; and U.S. Pat. No. 4,794,963 by Oppeneer. Commercial systems based on these patents are available for optimizing lumber production from flitches or cants, primarily for softwood lumber production. These commercial systems are based solely on board profile information; hence, these systems clearly cannot provide optimal performance where surface defects such as knots have a large influence on product value.
To detect those defects in wood that influence product value, much effort has also gone into lumber defect scanning systems, particularly in the production of structural softwood lumber. These systems, described by R. Szymani et al., "Defect detection in lumber: State of the art," 31 Forest Products Journal, pp. 34-44 (1981) ("Szymani et al."), employ scanning techniques including optical, slope-of-grain, microwave, ultrasonic, and x-ray sensing techniques. Optical scanning is disclosed in U.S. Pat. No. 4,286,880 to Young; U.S. Pat. No. 5,412,220 to Moore; U.S. Pat. No. 4,827,142 to Hatje; International Pat. publication No. WO 93/22659 to Nyquist; and International Pat. publication No. WO 95/24636 to Astrom et al. Slope-of-grain detection is disclosed in U.S. Pat. No. 4,926,350 to Bechtel et al.; U.S. Pat. No. 4,500,835 to Heikkila; and U.S. Pat. Nos. 3,976,384, 4,606,645 and 5,252,836 to Matthews et al. Microwave sensing is disclosed in U.S. Pat. No. 4,607,212 to Jakkula and U.S. Pat. No. 4,514,680 to Heikkila et al. X-ray scanning is disclosed in U.S. Pat. No. 4,941,357 to Schajer and E. German Patent No. 223 534 to Fischer et al. While each of these systems is specifically focused on detecting a particular feature in wood, none of these can precisely detect and classify all features that affect the value of lumber.
More recent systems in softwood lumber production and grading have been proposed to more precisely locate critical strength-reducing defects in lumber based on a combination of optical sensing techniques with one or more of the following: x-ray scanning, microwave scanning, deflection testing, capacitance sensing, and ultrasound. Such systems are described by Szymani et al.; D. J. Kenway et al., "Computer aided lumber grading," Proceedings of the 7th Symposium on Nondestructive Testing of Wood (Madison, Wis. 1990) ("D. J. Kenway et al."); and J. E. Aune, "X-ray edger-optimizer makes money at MacMillan Bloedel's Alberni Pacific Division," 4th International Conference on Scanning Technology in the Wood Industry (San Francisco 1991). Patents relating to research in this area combine optical profiling with x-ray scanning (disclosed in U.S. Pat. No. 4,879,752 and Canadian Pat. No. 1,281,392 to Aune et al.) and optical scanning with deflection testing (disclosed in U.S. Pat. No. 4,805,679 to Czinner). Even though these multi-sensor defect detection approaches can more precisely locate strength-reducing defects in wood, they have not been successfully used to detect all lumber surface features that affect its appearance quality.
While one softwood company is providing customers with machine evaluated lumber (MEL), there is no commercial system available that can structurally grade softwood lumber based on the location and identity of features in the wood as established by the Southern Pine Inspection Bureau, Western Wood Products Association, etc.
In hardwood lumber, it is the visual appearance of wood, rather than its strength, that affects its value. Some research has been performed to create systems for automating primary processing and grading of lumber, as described by Araman et al. and by R. W. Conners et al., "A machine vision system for automatically grading hardwood lumber," 2 Industrial Metrology, pp. 317-342 (1992) ("R. W. Conners et al. (I)"). However, no practical devices have been designed for solving either the edging and trimming optimization problem or the grading problem. Some hardwood sawmillers are using devices designed for softwood edging and trimming in their hardwood plants. These systems are costly and provide substantially suboptimal strategies for hardwood lumber processing and grading where visual appearance defects have a substantial affect on product value.
Researchers have also investigated the softwood plywood processing and grading problem, as discussed by D. A. Butler et al., "An adaptive image preprocessing algorithm for defect detection in Douglas-fir veneer," 43 Forest Products Journal, pp. 57-60 (1993); J. B. Forrer et al., "Image sweep-and-mark algorithms. Part 2. Performance evaluations," 39 Forest Products Journal, pp. 39-42 (1989); and C. R. Friedrich, "Development and simulation of machine automated green veneer sorting and defect identification," 4th International Conference on Scanning Technology in the Wood Industry (San Francisco 1991). Patent disclosures relating to commercial softwood veneer inspection include U.S. Pat. No. 3,694,658 to Watson et al. and U.S. Pat. No. 4,984,172 to Luminari. These patented systems can only detect defects involving discontinuities in the wood (holes, splits, voids, etc.). Surface defects such as knots, stains, and other such sound features cannot be reliably detected by these systems.
Finally, there are the applications within the secondary manufacturing area. The process of turning dried wood and veneer into a finished product is, as one might suppose, a difficult task, typically requiring a good number of processing operations. For purposes of this discussion, most work on developing machine vision technologies for aiding this industrial sector has concentrated on the initial sawing operations performed to turn lumber into the rough parts used to create all the components of the finished product. The place where this cutup occurs in a secondary manufacturing plant is called the rough mill.
Depending on the dimensions of the rough parts needed, rough mills are usually laid out in one of two ways. The first, more modern layout involves first sawing lumber into the desired widths needed using a gang-rip saw. The resulting lumber strips or lineals are then crosscut to the desired lengths. A second layout involves first crosscutting the lumber into the required lengths. This crosscutting operation is then followed by ripping each of the parts sawn to length into the parts that have the required widths. Obviously, in either instance the objective is to remove any undesirable wood features while maximizing yield.
Since the lumber raw material used by secondary manufacturers has already had a good deal of value added to it by the loggers and the primary manufacturers, the cost of this raw material is relatively high as compared to the raw materials used by primary processors. Because of the cost of raw material, a good deal of research effort has been expended to develop machine vision technologies to optimize the utilization of lumber. This research has been described by P. A. Araman et al.; R. W. Conners et al. (I); C. C. Brunner et al., "Using color in machine vision systems for wood processing," 22 Wood and Fiber Science, pp. 413-428 (1990); P. Klinkhachorn et al., "Prototyping an automated lumber processing system," 43 Forest Products Journal, pp. 11-18 (1993); A. J. Koivo et al., "Automatic classification of surface defects on red oak boards," 39 Forest Products Journal, pp. 22-30 (1989); C. W. McMillin, "Application of automatic image analysis to wood science," 14 Wood Science, pp. 97-105 (1982); C. W. McMillin et al., "ALPS--A potential new automated lumber processing system," 34 Forest Products Journal, pp. 13-20 (1984); R. W. Conners et al., "Identifying and locating surface defects in wood: Part of an automated lumber processing system," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-5, pp. 573-583 (1983) ("R. W. Conners et al. II"); and R. W. Conners et al., "The utility of color information in the location and identification of defects in surfaced hardwood lumber," 1st International Conference on Scanning Technology in Sawmilling (San Francisco, Calif. 1985) ("R. W. Conners et al. III").
Some of this research has found its way into commercial systems. However, these systems are typically based on a single sensing modality, e.g., one or more black-and-white cameras or one or more color cameras, and are only capable of sensing gross features in wood. Examples of applications where this is sufficient include the defect detection system for a gang rip saw and/or the cross cutting of softwood lumber for some product applications. It should be kept in mind that the automatic detection of features in softwood lumber is considered by researchers to be simpler problem than the automatic detection of features in hardwood.
A thorough review of the literature reveals that while rough mill automation can be economically justified, no commercial system is currently available that can automatically inspect lumber and detect all features that are necessary to completely optimize yield in the rough mill. State-of-the-art commercial systems still rely on operators to inspect lumber for critical defects. For example, state-of-the-art cross cut saws still rely on operators to mark defects with florescent crayons prior to cutup.
The prior art systems discussed above dealing with defect detection in primary processing and grading of lumber might have some applicability to defect detection for rough mill applications. However, in secondary lumber processing, a completely automatic lumber inspection system must not only accurately detect the size and shape of lumber, but also must accurately find the location and type of defect with sufficient precision and resolution. As mentioned earlier, none of the patented scanning technologies are robust enough to provide this level of detail in lumber defect identification. With wood being a very non-homogeneous and highly variable material, machine vision systems that can reliably detect lumber features have not yet been achieved.
To address the need for such robust machine vision systems for the lumber inspection problem, a combination of sensing methods must be applied. This fact was realized in 1981 by R. Szymani et al. Within the last five years, computing technology has made such a multi-sensing approach for lumber inspection a more realizable goal. Several researchers have worked in the area of multi-sensor scanning, as discussed by D. J. Kenway et al.; R. W. Conners et al. (I); J. F. Portala et al., "Nondestructive testing techniques applied to wood scanning," 2 Industrial Metrology, pp. 299-308 (1992); P. Rowa, "Automatic visual inspection and grading of lumber," 1st International Seminar on Scanning Technology and Image Processing on Wood (Lulea University, Skelleftea Sweden 1992); and O. Hagman et al., "Multivariate image analysis methods to classify features on scots pine: Evaluation of a multisensor approach," 5th International Conference on Scanning Technology and Process Control for the Wood Products Industry (San Francisco, Calif. 1993). Although some of the multi-sensor systems dealing with defect detection in primary processing and grading of lumber (for example, U.S. Pat. No. 4,879,752 and Canadian Pat. No. 1,281,392 to Aune et al.; U.S. Pat. No. 4,805,679 to Czinner) can be applied to rough mill automation, they still are not able to provide the precision and accuracy needed to detect all critical lumber defects.
From the above it should be clear that, in general, the automatic inspection of boards, lineals, cants or flitches requires three types of information, as described by R. W. Conners et al., "Developing a multi-sensor scanning system for hardwood inspection and processing," Proceedings from the 2nd International Seminar/Workshop on Scanning Technology and Image Processing on Wood (Skelleftea, Sweden 1995). First, automatic inspection requires information about the three-dimensional shape of the board, lineal, cant, or flitch. This information is needed to determine whether the object inspected is warped, contains wane, or contains areas that are too thin. Second, it requires information about the location and identity of internal features that, during further processing, could be exposed and make the processed part either unsuitable for further use or of decreased value. Finally, it requires information about the location and identity of surface features and/or discolorations. This last type of information is particularly important in many hardwood applications where the appearance of the product plays such an important role. Unfortunately, no single sensing modality can provide all of this needed information.
Perhaps the most mature technology for inspecting wood is that which can measure a board's, lineal's, cant's, or flitch's three dimensional profile. Patents which address the acquisition of this type of information include U.S. Pat. No. 4,123,169 to Merilainen et al., U.S. Pat. No. 4,188,544 to Chasson, U.S. Pat. No. 4,541,722 to Jenks, U.S. Pat. No. 4,984,172 to Luminari, U.S. Pat. No. 5,142,955 to Hale, International Patent No. WO 93/22659 to Nyquist, and E. German Patent No. 265 357 to Fischer et al. While these single sensing modality devices can gauge three-dimensional shape at varying degrees of resolution, they do not address ways for locating and identifying surface and/or internal features in wood. Surface and/or internal features present in a board, lineal, cant, or flitch are, in many cases, used to establish its value. Hence, these systems are seriously limited as to the types of inspection tasks they can perform.
As to the internal features of boards, lineals, cants, or flitches, a number of single sensor systems for addressing this issue have also been developed, including system which employ two different types of electromagnetic radiation sensing techniques. The first type uses microwaves. Microwave-based systems include U.S. Pat. No. 4,500,835 to Heikkila, U.S. Pat. No. 4,514,680 to Heikkila et al., and U.S. Pat. No. 4,607,212 to Jakkula. A fundamental problem with microwaves is that they cannot be used to detect any feature that has a diameter smaller than the wavelength of the radiation being employed. Hence, microwave-based systems have difficulty detecting features smaller than approximately 1/4 inch (0.635 cm) in diameter. Unfortunately, for most wood inspection tasks features of this size can and do play an important role in establishing value. Hence these systems are also very limited in the number of applications on which they can be used.
The second type uses x-rays. A number of single sensor x-ray systems have been developed, as exemplified in U.S. Pat. No. 4,941,357 to Schajer and E. German Patent No. 223 534 to Fischer et al. These systems employ a single x-ray source and set of detectors that take a single view of the object being inspected. One very fundamental limitation of this approach is that the system cannot distinguish areas of wane from areas of decay. This limitation markedly limits their utility in addressing the more general wood inspection problem.
A variation of the above x-ray-based approaches is described in International Patent No. WO 91/05245. This invention uses computed tomography (CT) to locate and identify features. That is, it uses x-rays to take several views of an object. These views can then be used to reconstruct a cross-section of the object. Consequently, this system can provide information not only about internal features but also about surface features and three-dimensional shape.
Unfortunately, this approach has a number of limitations. First, the throughput is limited by the need to take several views and the need to do reconstruction. CT reconstruction methods are fairly complex computationally, and require relatively expensive special purpose hardware. For these same reasons CT systems are expensive, too expensive for most application problems. Lastly, CT cannot sense color variations in an object and, hence, does not address this very important part of the wood inspection problem. The limitations in throughput, cost, and the inability to detect discoloration mean that CT-based approaches are not acceptable for most wood inspection problems.
As to the detection of surface features, a number of systems have been developed for this task, all employing only a single sensing modality. The sensors of choice are black-and-white cameras and color cameras. U.S. Pat. No. 4,827,142 to Hatje and U.S. Pat. No. 5,412,220 to Moore describe systems that employ such cameras. The major problems with these systems are that they cannot determine three-dimensional profile and they cannot locate and identity internal wood features.
Another problem is their innate lack of feature detection and identification accuracy. For example, many species of wood contain knots that are almost the same color as clear wood. Such knots pose problems for black-and-white camera or color camera-based systems. There are commercially available black-and-white camera-based systems for automating the gangrip operation in rough mills. These systems only have to locate and identify major defects and even if some errors are made, they do provide performance improvements over systems that perform the gangrip based solely on board edge information. Clearly, these single sensor-based systems cannot determine three-dimensional shape nor can they be used to locate and identify internal defects. They therefore address only part of the general inspection problem.
U.S. Pat. No. 3,694,658 to Watson et al. describes a system based on a black-and-white camera sensor. However, this system uses back lighting, lighting that does not illuminate the surface of the material that can be imaged by the camera. This invention's use of back lighting results from the desire to detect holes in veneer. Clearly, this technology is not applicable to wood surface defect detection problem. Similarly, as described in U.S. Pat. No. 4,468,992 to McGeehee, a system uses back lighting and an optical detector to measure only the width of a board. The capabilities of such a system are very limited in the area of general wood inspection.
A variant of the standard black-and-white/color camera-based systems is a system based on the so-called smart sensor, International Patent No. WO 95/24636. This smart sensor can gauge three-dimensional shape information while it is generating black-and-white imagery. As such, it does offer an improvement over the above. However, this sensor still has difficulties with knots that are the same color as clear wood and, of course, this sensor cannot be used to locate and identify internal defects. This imposes limitations on any system that uses only this scanning modality.
Yet another variant on the basic black-and-white/color camera-based systems, is the one described in U.S. Pat. No. 4,207,472 to Idelsohn et al. and U.S. Pat. No. 4,221,974 to Mueller et al. This system uses a flying-spot of laser light to illuminate board surfaces. Imagery is generated using photo diodes to sense the light. By carefully controlling the speed of the laser spot and the times at which the photo diodes are read, two-dimensional black-and-white image data can be collected,. Because this data is similar in content to that generated by a black-and-white camera, this system suffers from the same problems discussed above for single sensor black-and-white-based systems.
Another, totally different approach to surface feature location and identification involves the use of sensors that can detect the slope-of-grain. Systems based on slope-of-grain detection sensors include U.S. Pat. No. 3,976,384, U.S. Pat. No. 4,606,645, and U.S. Pat. No. 5,252,836 to Matthews et al., and U.S. Pat. No. 4,926,350 to Bechtel et al. These systems also have limitations. First, they cannot determine three-dimensional shape. Second, they cannot locate and identify internal defects. Finally, they are unable to detect the important discolorations that occur in wood.
The realization that no single sensing technology is adequate for wood inspection has motivated inventors to create systems that employ multiple sensing modalities. For example, the system described in U.S. Pat. No. 4,805,679 to Czinner employs a black-and-white camera together with a device for measuring the modulus of elasticity. While this system might be useful for some wood inspection applications, it is very limited by its inability to determine three-dimensional shape and locate and identify internal features.
Yet another multiple sensor system is described in U.S. Pat. No. 4,831,545 to Floyd et al. This system employs the slope-of-grain detection sensor described in U.S. Pat. No. 4,606,645 to Matthews ct al. with a sensor for detecting wane, i.e., a sensor for gauging rough three dimensional shape. This system has a number of problems with regard to the general wood inspection problem. First, even with the addition of the second sensor, it still cannot determine certain types of warp or areas that are too thin. Second, it cannot locate or identify internal features. Third, it cannot detect surface discolorations. This limits the number of applications to which this technology can be applied.
Three other, seemingly related, multiple sensor systems are described in U.S. Pat. Nos. 4,879,752 and 5,023,805 to Aune et al., U.S. Pat. No. 5,394,342 to Poon, and Canadian Patent No. 1,281,392. U.S. Pat. No. 5,023,805 and U.S. Pat. No. 5,394,342 describe a system for inspecting logs. This system employs two different sensing modalities. It uses a laser-based system to determine three-dimensional log shape and three x-ray sources looking at the log from three different directions to locate internal knots. U.S. Pat. No. 4,879,752 and Canadian Patent No. 1,281,392 describes a system for lumber inspection. This system uses a profiler for finding wane and an x-ray source to find defects in lumber. The lumber inspection system is limited because it cannot detect whether a feature is a surface feature or a purely internal feature, an important differentiation in some applications, nor can this system determine areas of discoloration. Hence, it, too, is limited in the variety of applications in which it can be used.
It is to the solution of these and other problems that the present invention is addressed.